How to measure AI in finance
Measuring AI in finance requires a combination of leading and lagging measures, such as variance to budget and commitment coverage, to accurately evaluate the impact of AI initiatives on financial performance, and to identify areas where AI can improve financial decision-making and reduce costs.
Can we trust the metrics we use to measure the impact of AI in finance, or are they prone to being influenced by unrelated factors, leading to incorrect conclusions about the effectiveness of our AI initiatives? This is a question that finance teams often grapple with when trying to evaluate the success of their AI projects.
Leading measures: variance to budget, commitment coverage
One approach to measuring the impact of AI in finance is to focus on leading measures that are directly related to financial performance. Two such measures are variance to budget and commitment coverage. Variance to budget measures the difference between actual and budgeted expenses, while commitment coverage measures the extent to which an organisation's financial commitments are covered by its available funds. These measures are useful because they provide an early indication of whether an AI initiative is having the desired impact on financial performance.
A shift supervisor at a manufacturing plant, for example, might use AsscherAi to analyse the maintenance log and identify areas where AI-powered predictive maintenance can help reduce downtime and lower maintenance costs. By tracking variance to budget and commitment coverage, the supervisor can quickly determine whether the AI initiative is having a positive impact on the plant's financial performance.
Why the headline metric moves for unrelated reasons
Most people get wrong the idea that a single headline metric, such as return on investment (ROI), can accurately capture the impact of AI on financial performance. The problem with this approach is that ROI can be influenced by a wide range of factors that have nothing to do with the AI initiative itself, such as changes in market conditions or unexpected one-time expenses. As a result, it is possible for the ROI metric to move for reasons that are unrelated to the AI initiative, leading to incorrect conclusions about its effectiveness.
This is why it is so important to use a combination of leading and lagging measures to evaluate the impact of AI on financial performance. By using multiple measures, finance teams can get a more complete picture of whether an AI initiative is having the desired impact, and make adjustments as needed to ensure that the initiative stays on track.
Baselining before rollout
Another key consideration when measuring the impact of AI in finance is the importance of baselining before rollout. This involves collecting data on current financial performance before the AI initiative is implemented, in order to establish a baseline against which future performance can be compared. Without a baseline, it is impossible to determine whether any changes in financial performance are due to the AI initiative or other factors.
Baselining is particularly important when using AI to analyse large datasets, as it allows finance teams to identify areas where the AI system is having a positive impact, and make adjustments to the system as needed to ensure that it continues to deliver value. For more information on how to use AI in finance, visit our finance page.
What to record when nothing was found
Even when an AI initiative does not identify any significant issues or opportunities for improvement, it is still important to record the results of the analysis. This can help to establish a record of what was done, and provide a basis for future analyses. It can also help to identify areas where the AI system may need to be refined or improved, in order to deliver more accurate and useful results.
In some cases, the fact that nothing was found can be just as valuable as finding something. For example, a category manager who uses AsscherAi to analyse supplier data may find that all suppliers are meeting their contractual obligations, which can provide a sense of comfort and reassurance that the supply chain is operating smoothly.
What this does not do
This approach to measuring the impact of AI in finance is not a substitute for human judgement and expertise. While AI can provide valuable insights and analysis, it is still important for finance teams to use their own expertise and experience to interpret the results and make decisions. Additionally, this approach may not be suitable for all types of AI initiatives, such as those that involve complex or nuanced decision-making.
If you have any questions about how to measure the impact of AI in finance, or would like to learn more about how AsscherAi can help, please contact us to speak with one of our experts. We can help you to develop a tailored approach to measuring the impact of AI on your organisation's financial performance, and provide guidance on how to get the most out of your AI initiatives.
Frequently asked questions
What are the most effective metrics for measuring the impact of AI in finance?
Leading measures such as variance to budget and commitment coverage provide an early indication of AI's impact on financial performance.
How can finance teams ensure that their AI metrics are accurate and reliable?
Baselining before rollout and using multiple measures help to establish a clear picture of AI's impact and reduce the influence of unrelated factors.
What is the importance of recording results even when an AI initiative does not identify significant issues?
Recording results helps to establish a record of what was done and provides a basis for future analyses, and can also identify areas for AI system refinement.
Can AI metrics replace human judgement and expertise in financial decision-making?
No, AI metrics are not a substitute for human judgement and expertise, and finance teams should use their own expertise to interpret results and make decisions.